r Human Brain Mapping 31:1327–1338 (2010) r

Correlation Between Quantitative EEG and MRI inIdiopathic Generalized Epilepsy

Luiz E. Betting,1 Li M. Li,1 Iscia Lopes-Cendes,2 Marilisa M. Guerreiro,1

Carlos A.M. Guerreiro,1 and Fernando Cendes1*

1Department of Neurology, Faculty of Medical Sciences, University of Campinas—UNICAMP,Campinas, SP, Brazil

2Department of Medical Genetics, Faculty of Medical Sciences, University of Campinas—UNICAMP,Campinas, SP, Brazil

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Abstract: The objective of this study was to investigate the relationship between the focal discharges some-times observed in the electroencephalogram of patients with idiopathic generalized epilepsies and subtlestructural magnetic resonance imaging abnormalities. The main hypothesis to be assessed is that focal dis-charges may arise from areas of structural abnormality which can be detected by quantitative neuroimag-ing. Focal discharges were used for quantitative electroencephalogram source detection. Neuroimaginginvestigations consisted of voxel-based morphometry and region of interest volumetry. For voxel-basedmorphometry, volumetric MRI were acquired and processed. The images of each patient were individu-ally compared with a control group. Statistical analysis was used to detect differences in gray matter vol-umes. Region of interest-based morphometry was automatically performed and used essentially toconfirm voxel-basedmorphometry findings. The localization of the focal discharges on the electroencepha-logram was compared to the neuroimaging results. Twenty-two patients with idiopathic generalized epi-lepsies were evaluated. Gray matter abnormalities were detected by voxel-based morphometry analysis in77% of the patients. There was a good concordance between EEG source detection and voxel-based mor-phometry. On average, the nearest voxels detected by these methods were 19 mm (mm) apart and the moststatistically significant voxels were 34 mm apart. This study suggests that in some cases subtle gray matterabnormalities are associated with focal epileptiform discharges observed in the electroencephalograms ofpatients with idiopathic generalized epilepsies.Hum BrainMapp 31:1327–1338, 2010. VC 2010Wiley-Liss, Inc.

Keywords: epilepsy; neuroimaging; EEG source imaging; idiopathic generalized epilepsy; magneticresonance

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INTRODUCTION

Idiopathic generalized epilepsies (IGEs) are a group ofepilepsies characterized by seizures with generalized onset[Commission on Classification and Terminology of theInternational League Against Epilepsy, ILAE, 1989]. Aninter-ictal electroencephalogram (EEG) with typical gener-alized spike and wave (GSW) discharges and a normalbackground is supportive of IGE diagnosis [Panayiotopou-los, 2002]. Despite the criteria for diagnoses, focalities arenot rare in the interictal EEG records of patients with IGEand several hypotheses have been proposed to explaintheir presence [Lombroso, 1997; Panayiotopoulos, 2002;

Additional Supporting Information may be found in the onlineversion of this article.

Contract grant sponsor: Fundacao de Amparo a Pesquisa doEstado de Sao Paulo (FAPESP).

*Correspondence to: Fernando Cendes, MD, PhD, Department ofNeurology, Faculty of Medical Sciences, PO Box 6111, University ofCampinas—UNICAMP, 13083-970, Campinas, SP, Brazil.E-mail: fcendes@unicamp.br

Received for publication 22 February 2009; Revised 6 October2009; Accepted 7 October 2009

DOI: 10.1002/hbm.20944Published online 15 January 2010 in Wiley Online Library(wileyonlinelibrary.com).

VC 2010 Wiley-Liss, Inc.

Waltz, 2000]. Focalities may represent fragmented general-ized discharges or focal cortical pathology, or they mayrepresent localized self-sustaining hyperexcitability or beincidental findings [Lombroso, 1997]. The underlying etiol-ogy of focal discharges is currently under debate.

The pathophysiology of the GSW discharges involves anextensive network and mainly the thalamo-cortical circuitry.Challenging the assumption of diffuse cortical hyperexcit-ability related to the onset of the generalized seizures, ex-perimental studies have suggested that these seizures havea focal background [Gloor, 1968; Meeren et al., 2002]. There-fore, defining the etiology of the EEG focalities observed inpatients with IGE will provide an understanding of themechanisms of these disorders and may confirm whethergeneralized seizures have a focal component.

Previous investigations using quantitative magnetic res-onance imaging (MRI) detected subtle structural abnormal-ities in patients with IGE [Betting et al., 2006a; Woermannet al., 1999]. The combination of different imaging techni-ques may improve the detection of epileptogenic lesionsand allow more precise assessment of neuronal function[Koepp and Woermann, 2005]. The objective of this studywas to determine the relationship between focal EEG dis-charges and subtle structural MRI abnormalities inpatients with IGE. The main hypothesis to be investigatedis that focal discharges may arise from areas of structuralabnormality which can be detected by multimodal quanti-tative neuroimaging.

METHODS

Subjects

Based on the presence of focalities in the EEG, 22 (12women, mean age 30 � 11 years, range 9–60) subjects wereconsecutively selected from 216 patients evaluated withIGE diagnosis. Patients were classified in three groups: ju-venile myoclonic epilepsy (JME), epilepsy with generalizedtonic-clonic seizures on awakening (GTCS) and absenceepilepsy (AE) which included childhood absence epilepsy(CAE) and juvenile absence epilepsy. Patients classificationwas performed according to clinical and EEG criteria[ILAE, 1989; Panayiotopoulos, 2002]. All patients and oneindividual who had witnessed a typical seizure were rein-terviewed, and the medical records were analyzed.

EEG Recording Protocol

All EEGs were performed in the interictal state. A 32-channel Nihon-Kohden EEG recorder was used with adigitization rate of 200 Hz. The ground electrode wasplaced at Fpz, and the band pass was situated at 0.3–70 Hz. In addition to the traditional 19 electrodes used inthe international 10–20 system for electrode placement,two zigomatic fronto-temporal electrodes were alsoincluded for finer coverage of the temporal regions. The

recording was made according to previous recommenda-tions and included 20-min extent, hyperventilation andphotic stimulation. At least one EEG record from eachpatient had the typical GSW discharges with normal back-ground. The patients included in the study also had aminimum of one record with a clear focal or generalizedasymmetrical epileptiform discharge. For this study, slowwaves were not classified as focalities.

EEG Processing

EEG focalities or asymmetries were visually selectedand stored using Neurofax software (EEG-1000, Version04-01, Nihon-Kohden Corporation, Tokyo, Japan). Seg-ments with artifacts were rejected from the analysis. Thefinal preparation aligned the EEG segments, centered atthe peak of the focal discharge with the higher amplitude,which was observed in the referential montage (with aver-age). The segments were stored as two seconds durations(400 sample points). For the patients with more than onefocal discharge, the selected segments were averaged. If apatient presented discharges in different locations, thesefocalities were individually analyzed.

EEG Source Detection

For localization, the Standardized Low Resolution BrainElectromagnetic Tomography (sLORETA) software wasused [Pascual-Marqui, 2002]. This evaluation was per-formed for patients who had abnormalities detected dur-ing quantitative MRI analysis. Raw time-domain currentdensity distributions were acquired for all of the individ-ual two seconds epochs (time frames �1,000 to þ1,000ms). Such raw sLORETA values represent the power (thesquared magnitude of the computed intracerebral currentdensity) within 6,430 voxels at a 5-mm spatial resolution[Pascual-Marqui, 2002]. The grid in use is based on thedigitized Talairach atlas [Talairach and Tournoux, 1988].For source localization, sLORETA solutions were calcu-lated for the average ascending phase (20 ms) of the focaldischarge of interest. Previous investigations demonstratedthat modeling in the ascending phase of the spike producemore reliable source identification [Huppertz et al., 2001;Scherg et al., 1999]. Region of interest (ROI) analysis wasalso conducted using the built-in sLORETA routines.There were 42 ROIs based on the Brodmann’s areas (BAs)that were selected for each cerebral hemisphere. TheseROIs were composed of a single centroid voxel locatedclose to the center of each BA mass.

MRI Acquisition Protocol

Volumetric (3D) T1-weighted images with 1 mm iso-tropic voxels were acquired in a 2T (GE Elscint, Haifa,Israel) scanner using a spoiled gradient echo sequence

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with flip angle ¼ 35�, repetition time (TR) ¼ 22 ms, echotime (TE) ¼ 9 ms, matrix ¼ 256 � 220, field of view (FOV)¼ 23 � 25 cm and 1-mm thick sagittal slices. Thissequence was used for VBM analysis.

Image Processing

The images were acquired in the Digital Imaging andCommunications in Medicine (DICOM) format. MRIcrosoftware was used to transform the images into the ANA-LYZE format for the next steps [Rorden and Brett, 2000].SPM5 software (Wellcome Trust Centre for Neuroimaging,London, England; available at: www.fil.ion.ucl.ac.uk) andVBM5 toolbox (available at: http://dbm.neuro.uni-jena.de)were used for image processing and statistical analysis.VBM5 toolbox employs a new unified segmentationapproach which integrates image registration, MRI inho-mogeneity bias correction and tissue classification [Ash-burner and Friston, 2005; Meisenzahl et al., 2008]. Allimages underwent spatial normalization. Normalizationreduces the interindividual variation due to brain size andpositioning. Normalization was performed registering eachimage to the template of the International Consortium forBrain Mapping (Montreal Neurological Institute, Montreal,Canada). First, a 12-parameter affine transformation wasused. Images were modulated in order to preserve tissuethat was eventually deformed by the normalization proto-col. Finally, the images were warped using nonlinear spa-tial transformations. Normalized images were correctedfor non-uniformities in signal intensities and then submit-ted to automatic segmentation of the cerebrospinal fluid,white and gray matter, without the use of tissue priors.This approach reduces the population-specific bias andincreases tissue classification accuracy. By applying a Hid-den Markov Field model on segmented maps, VBM5 tool-box extends the unified segmentation approach andimproves the quality of segmentation [Meisenzahl et al.,2008]. The algorithm used removes voxels that were iso-lated and are unlikely to be a member of a certain tissueclass and close holes present in clusters of connected vox-els resulting in a higher signal-to-noise ratio of the tissueprobability maps [Meisenzahl et al., 2008]. For the presentinvestigation, gray matter maps were used for VBM analy-sis. The final processing step consisted in smoothing theimages convolving them with an 8-mm full-width at halfmaximum isotropic Gaussian kernel to reduce interindi-vidual gyral variation.

MRI ROI Analysis

A ROI-based morphometric analysis was also conductedto strengthen the relationship between the EEG quantita-tive source analysis and the neuroimaging investigations.The Individual Brain Atlases using Statistical ParametricMapping Software (IBASPM) toolbox was used to extract

the volumes of 42 brain structures in each hemisphere.[Aleman-Gomez et al., 2006]. For this evaluation, we usedan atlas of 84 segmented structures included in theIBASPM software. The volumes in cm3 obtained for eachindividual were normalized based on the total intracranialvolume of the controls. Next, the volumes were standar-dized relative to the volumes of normal controls subjectsusing a Z-score transformation.

VBM Statistical Analysis

The MRI images from each patient were individuallycompared to a control group of 20 healthy volunteers (10women, mean age 31 � 8, range 22–52). Each subject inthe control group was also individually compared withthe remaining 19 using analysis of variance and no graymatter abnormalities were detected. All patients and con-trols signed informed consent forms for participation. Thestudy was approved by the local ethics committee. Avoxel-by-voxel analysis of covariance (AnCova) was usedto test for differences between the gray matter volumes ofeach patient and the control group. Age and gender wereentered as covariates in the statistical design. The analysisincluded global grand mean scaling and proportionalthreshold masking of 0.4. The significance thresholdselected was P < 0.05 corrected for multiple comparisons(false discovery rate).

EEG and VBM Distance Analysis

The Euclidian distances between the nearest voxel withthe highest current density from the nearest suprathres-hold voxel and the distance of the voxel with the highestcurrent density from the most statistically significant voxel(nearest local maxima) were calculated. A 3D render of thebrain overlaid with a graphical representation of theregion with highest current density detected by sLORETAand the clusters of VBM statistically significant voxels wasconstructed to assist in the interpretation of the distancebetween the methods.

RESULTS

Clinical Features

Seven patients had JME (five women, mean age 26 � 10years, range 15–46). One of these patients presented myo-clonic, absence and generalized tonic-clonic seizures, fivepresented myoclonic and generalized tonic-clonic seizures,and one presented myoclonic seizures only. Focal semiol-ogy suggestive of auras or partial complex seizures wasdescribed in four (57%) patients (Table I).

Eleven patients had AE (four women, mean age 22 � 13years, range 9–60). Two of these patients presented myo-clonic, absence and generalized tonic-clonic seizures, seven

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presented absence and generalized tonic-clonic seizures,and two presented absence seizures only. Focal semiologysuggestive of auras or partial complex seizures wasdescribed in two (18%) patients (Table I).

Four patients had GTCS (three women, mean age 28 � 7years, range 19–35). One patient presented myoclonic andgeneralized tonic-clonic seizures, and three presented gen-eralized tonic-clonic seizures only. Focal semiology sug-gestive of auras or partial complex seizures was notdescribed in any of the GTCS patients (Table I).

Head injury and other risk factors for epilepsy wereinvestigated. Only two patients have a positive history forhead injury. One of these had a skull fracture at 7 yearswith no neurological complications. The other patientdescribed a minor trauma with temporary loss of con-science. In both patients, there was no evidence of lesionin the visual analysis of the MRI.

EEG Features

Ninety-three EEG records were reevaluated. A mean of4 � 2 EEG recordings were performed on each patient(range 1–9). There were 26 EEG exams performed in theJME group, and 12 (46%) showed focalities. The persist-ence of focalities ranged from 8 months to 5 years. In three

patients, focalities were present in only one record. Twopatients presented different focality patterns in their EEGprofiles. There were 43 EEG exams performed in the AEgroup, and 17 (39%) showed focalities. The persistence offocalities ranged from 1 month to 4 years. In sevenpatients, focalities were present in only one record. Threepatients presented different focality patterns in their EEGprofiles. There were 24 EEG exams performed in theGTCS group, and 11 (45%) showed focalities. The persist-ence of focalities ranged from 1 month to 9 years. In onepatient, focalities were present in only one record. Onepatient presented different focality patterns in their EEGprofiles. Table II summarizes these findings.

EEG Source Detection

In total, 117 focal discharges of 17 patients were eval-uated (mean 5 � 9, range 1–46). Four patients had only asingle epileptiform discharge available for source analysis.Six patients had discharges in different locations. Sourceanalysis was performed in 23 processed discharges. Table IIIshows the areas of current density maxima with the respec-tive coordinates. Figures 1 and 2 and Supporting Informa-tion, Figures 1–3 show the discharges evaluated (Parts Aand C) and an example of the generalized discharge (Part

TABLE I. Clinical features of 22 IGE patients grouped by subsyndrome classification

Pat Family history Agea Onseta sz Lasta sz Gender Seizure type AED Syndrome

3 Mother, brother, nephew 46 46 0 W M, G CBZ JME7 Negative 33 23 10 M M, G VPA JME8 Four cousins 29 13 1 W M, A, G VPA JME15 Father, sister 18 7 2 M M, G VPA JME16 Mother, grandfather and uncle 15 14 0 W M VPA JME19 Uncle, sister 22 22 0 W M, G None JME22 Sister 23 9 1 W M, G CBZ JME4 Negative 31 18 0 M A, G VPA JAE5 Uncle 21 14 1 M A, G VPA JAE6 Uncle, ant, cousin 22 11 0 M A, G LMT JAE10 Grandfather, uncle 10 8 1 W A, G VPA CAE11 Negative 60 29 1 M M, A, G VPA JAE12 Negative 20 13 0 M A VPA JAE13 Negative 16 10 2 M A, G VPA JAE14 Grandmother 9 2 0 W A ETX CAE17 Uncle, grandmother 22 4 1 W M, A, G VPA JAE18 Sister 18 6 0 M A, G VPA JAE21 Two uncles 19 16 2 W A, G VPA JAE1 Father, sister 35 11 1 M G CBZ GTCS2 Negative 29 15 1 W G CBZ GTCS9 Uncle 19 7 1 W M, G CBZ GTCS20 Two sisters, ant, nephews 32 13 2 W G DPH GTCS

The MRI VBM analyses of patients 3, 7, 9, 13 and 15 presented no abnormalities when individually compared with the controls.Family history, family members with epilepsy; Pat, patient; M, man; W, woman; M, myoclonic; A, absence; G, generalized tonic-clonicseizures; AED, antiepileptic drug; VPA, valproate; CBZ, carbamazepine; DPH, phenytoin; ETX, ethossuximide; JME, juvenile myoclonicepilepsy; CAE, childhood absence epilepsy; JAE, juvenile absence epilepsy; GTCS, generalized tonic-clonic seizures on awakening; sz,seizure.aExpressed in years.

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B). The sLORETA source solutions were overlaid with a T1MRI anatomical template and with 3D render (Figs. 1 and2 and Supporting Information, Figs. 1–3, Part E, superiorand middle panels, respectively). Part D of the figuresshows the results of the EEG ROI analysis. A spatiotempo-ral map of the current density was constructed based onthe raw current density values of timeframes 150–300[Zumsteg et al., 2006]. The current values were color-codedto facilitate visualization, and the arrows indicate the localof current maxima obtained using the average technique[Zumsteg et al., 2006].

MRI and Voxel-Based Morphometry

(VBM) Analysis

All MRI images were visual analyzed, and no significantabnormalities were observed. The VBM comparisonsshowed areas of gray matter abnormalities in 17 of the 22(77%) patients evaluated. Fifteen of the 17 (88%) patientsshowed areas of increased gray matter volumes. The othertwo (12%) patients showed a mixed pattern of atrophyand increased gray matter volume.

Juvenile myoclonic epilepsy

Four of the seven (57%) patients had areas of abnormalgray matter. The areas of abnormal gray matter werelocated in the right rectal, inferior, middle and medialfrontal gyrus and in the left postcentral gyrus, parietal andoccipital lobe (posterior portion of the middle temporalgyrus).

Absence epilepsy

Ten of the 11 (91%) patients had areas of abnormal graymatter. The areas of abnormal gray matter were located inthe right superior, medial, middle and inferior frontalgyrus, postcentral gyrus, fusiform gyrus, cingulated gyrusand parietal lobe and in the left parietal lobe, middle andmedial frontal gyrus, middle and superior temporal gyrus.

Generalized tonic-clonic seizures

Three of four (75%) patients had areas of abnormal graymatter. The areas of abnormal gray matter volume were

located at the left inferior frontal gyrus, right insula andright parietal lobe.

Table III shows the areas of gray matter abnormalities,which were detected by the VBM analysis, with their re-spective coordinates. Figures 1 and 2 and Supporting In-formation, Figures 1–3 (Part E, inferior panel) displaysthese areas overlaid with an anatomical T1 MRI template.

ROI Volumetry Analysis

In 12 of the 17 (70%) patients the ROI volumetry analy-sis showed analogous results to the EEG source detectionand VBM (patients 2, 4, 6, 8, 10, 12, 14, 16, 19, 20, 21 and22). In seven of the 17 patients (41%), the thalamic vol-umes were abnormal. Atrophy was observed in threepatients (bilateral in two), and increased thalamic volumeswere observed in four patients (unilateral). Figure 3 andSupporting Information, Figure 4 show the results of themeasured volumes. Standardized volumes and detailsabout segmented structures are provided in SupportingInformation, Table I.

EEG and VBM Distance Analysis

The mean distance of the nearest voxel with the highestcurrent density detected by sLORETA from the VBM near-est suprathreshold voxel was 19 � 20 mm (range 1–98) forall the evaluated processed discharges, 17 � 14 (1–38) forJME patients, 20 � 22 (5–98) for AE patients and 23 � 19(6–44) for GTCS patients. The mean distance between thevoxel with the highest current density detected by sLOR-ETA from the most statistically significant voxel (nearestlocal maxima) detected by VBM was 34 � 17 (12–98) forall the evaluated processed discharges, 31 � 9 (17–38) forJME patients, 36 � 19 (14–98) for AE patients and 33 � 24(12–59) for GTCS patients. The individual distances aredescribed in Table III. Figure 4 and Supporting Informa-tion, Figure 5 show the voxels with highest current densitydetected by sLORETA and the clusters of VBM statisticallysignificant voxels in a 3D brain model.

DISCUSSION

The present work yielded two major findings. The firstmajor finding is that 77% of patients with IGE and EEG

TABLE II. General EEG features observed in 22 IGE patients

JME AE GTCS Total

Total of EEGs performed (n) 26 43 24 93Focalities 12 (46%) 17 (39%) 11 (45%) 40 (43%)Persistence of locationa 3 patients 4 patients 3 patients 10 patients

JME, juvenile myoclonic epilepsy; AE, absence epilepsy; GTCS, generalized tonic-clonic seizures on awakening.aDefined as the presence of a focality detected in the same location in two distinct EEG records, regardless of the time interval betweenrecords.

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TABLE III. Results of EEG source analysis and voxel-based morphometry of 17 patients with IGE

P#

EEG/sLORETA MRI/VBM

DX Y Za Localization Valueb X Y Z Localization P-value Cluster

8 �65 �55 15 Superior temporal gyrus 0.81 �69 �19 25 Postcentral gyrus 0.03 3860 38 A/B16 �35 �85 30 Occipital lobe 0.07 �33 �63 19 Occipital lobe 0.001 1252 25 A

�35 �80 40 Parietal lobe 0.07 �32 �61 11 Occipital lobe 35 B

19 10 20 �30 Rectal gyrus 0.57 9 9 �26 Rectal gyrus 0.006 628 11 A�60 �65 5 Middle temporal gyrus 0.21 �59 �65 5 Middle temporal gyrus 0.03 10852 1 A10 20 �30 Rectal gyrus 0.57 9 5 �23 Medial frontal gyrus 17 B

�40 �85 30 Occipital gyrus 0.21 �42 �64 48 Parietal lobe 28 B22 30 40 �5 Middle frontal gyrus 0.35 30 42 6 Middle frontal gyrus 0.007 181 12 A

25 35 �5 Inferior frontal gyrus 0.36 28 7 19 Inferior frontal gyrus 37 B4c 40 50 �15 Middle frontal gyrus 0.12 24 50 �10 Middle frontal gyrus <0.001 6132 17 A

�35 55 �10 Middle frontal gyrus 0.23 �27 55 �10 Middle frontal gyrus 0.01 6574 9 A

50 45 �10 Inferior frontal gyrus 0.13 18 49 �11 Middle frontal gyrus 32 B�40 55 �10 Middle frontal gyrus 0.23 �51 33 2 Middle frontal gyrus 27 B

5 35 40 �10 Middle frontal gyrus 0.87 28 40 �2 Middle frontal gyrus 0.001 1061 11 A�30 40 �15 Middle frontal gyrus 3.0 �32 44 �7 Middle frontal gyrus 0.001 434 9 A35 40 �10 Middle frontal gyrus 0.87 27 34 14 Inferior frontal gyrus 26 B

�30 35 �15 Middle frontal gyrus 3.0 �38 �3 �25 Middle frontal gyrus 40 B6 25 40 �10 Middle frontal gyrus 2.2 30 38 14 Middle frontal gyrus 0.002 294 25 A

�35 20 �25 Superior temporal gyrus 0.06 �37 �2 �21 Superior temporal gyrus 0.002 1113 23 A15 45 �25 Orbital gyrus 1.2 27 34 14 Medial frontal gyrus 42 B

�20 35 �25 Orbital gyrus 0.59 �38 �3 �25 Middle temporal gyrus 42 B

10 40 �30 20 Insula 0.42 43 �23 29 Postcentral gyrus <0.001 5336 12 A60 �5 �30 Fusiform gyrus 0.50 53 �40 �35 fusiform gyrus 36 B

11 25 55 �15 Superior frontal gyrus 0.98 20 53 �13 superior frontal gyrus 0.001 332 5 A

�35 45 15 Middle frontal gyrus 0.73 �18 45 4 Medial frontal gyrus <0.001 4235 20 A30 55 �15 Superior frontal gyrus 0.99 18 49 �12 Superior frontal gyrus 14 B

�35 50 30 Superior frontal gyrus 0.84 �26 14 32 Middle frontal gyrus 37 B12 �35 55 �10 Middle frontal gyrus 0.97 �39 41 �13 Middle frontal gyrus 0.03 187 14 A

�25 60 �15 Superior frontal gyrus 0.99 �41 38 �14 Middle frontal gyrus 27 B

14 45 �25 55 Parietal lobe 0.73 33 �32 62 Parietal lobe 0.04 842 15 A�55 �40 35 Parietal lobe 2.4 �40 �44 30 Parietal lobe 0.01 650 16 A

60 �25 50 Postcentral gyrus 0.76 25 �27 42 Cingulated gyrus 36 B�50 �35 60 Parietal lobe 2.5 �33 �25 30 Parietal lobe 36 B

17 25 55 �5 Middle frontal gyrus 0.26 24 41 �5 Middle frontal gyrus <0.001 3373 14 A

25 65 �5 Superior frontal gyrus 0.27 22 41 �3 Middle frontal gyrus 24 B18 45 45 �15 Middle frontal gyrus 0.44 36 �48 13 Postcentral gyrus 0.01 1925 98 A/B

21 �5 20 �20 Medial frontal gyrus 0.15 0 20 �12 Medial frontal gyrus <0.001 37535 9 A�5 25 �25 Rectal gyrus 0.16 �11 6 �22 Medial frontal gyrus 20 B

1 �30 10 �20 Inferior frontal gyrus 0.87 �37 27 �14 Inferior frontal gyrus 0.02 524 20 A

�35 5 �20 Superior temporal gyrus 0.89 �40 31 �12 Inferior frontal gyrus 28 B2 45 35 20 Middle frontal gyrus 0.24 26 �4 18 Insula 0.002 1967 44 A

50 40 20 Middle frontal gyrus 0.26 20 �8 5 Insula 59 B20 30 �50 65 Superior parietal lobule 0.04 28 �48 59 Superior parietal lobule 0.006 157 6 A

35 �45 65 Postcentral gyrus 0.04 28 �49 56 Parietal lobe 12 B

Patients are listed according to subsyndrome classification described in Table I. The distances between the nearest voxel with the high-est current density from the VBM nearest suprathreshold voxel (A) and the distance of the voxel with the highest current density fromthe most statistically significant voxel (B, nearest local maxima) detected by VBM are depicted. VBM analysis was performed by search-ing for gray matter concentration abnormalities. The statistical level of significance was P < 0.05 corrected for multiple comparisons(false discovery rate).P#, patient number; D, euclidian distance between the sLORETA and VBM voxel (mm).aX, Y and Z represent Montreal Neurological Institute coordinates expressed in millimeters (mm).bCurrent density value in miliamperes/mm2.cPatients with a mixed pattern of atrophy and increased gray matter volume.

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focalities have an area of subtle structural abnormality,which can be detected by VBM analysis. Secondly, weobserved a good concordance between the source analysisof the EEG focalities and the MRI gray matter abnormal-ities which were detected by VBM. Therefore, our studysuggests that anomalous gray matter volumes present inthe brains of some IGE patients are related with the focalinterictal epileptiform discharges observed in their EEGs.This observation is strong evidence which supportsthe postulated hypothesis that in some patients with IGE,focal EEG discharges may arise from subtle structuralabnormalities.

sLORETA was validated as a reliable method for sourcelocalization [Grova et al., 2006; Zumsteg et al., 2006]. The av-erage technique we used may sometimes falsely localize theepileptic source, especially when located in deep structures[Zumsteg et al., 2006]. The reduced number of electrodesand the sampling rate also may influence the EEG sourcedetection. Furthermore, in the present investigation, only

routine EEGs were evaluated and in four cases only one dis-charge was available for source analysis. In these situations,the noise level may be elevated reducing localization accu-racy. Nevertheless, there was a good concordance betweenthe methods with an average of 20 mm for the nearest vox-els and 34 mm for the voxels with maximum values. Thebest correlation between neuroimaging and EEG studieswas 10–16 mm described in evoked potentials experiments[Grova et al., 2008]. In the evaluation of the generators ofinterictal spikes, previous investigations using EEG-fMRIand EEG source localization showed a distance greater than30 mm between the methods [Bagshaw et al., 2006]. More-over, evaluating the distance between the nearest voxelwith the highest current density and VBM nearest supra-threshold voxel, 78% (18 of 23) of the processed dischargeswere in the same anatomical structure and 74% (17 of 23)had a distance inferior than 20 mm which is in the range ofthe sLORETA resolution that is presumed to be between 10and 20 mm [Mulert et al., 2004].

Figure 1.

Two EEG samples and source analysis and individual VBM com-

parisons of IGE patients with 20 controls searching for gray mat-

ter abnormalities. The data for patients 4 (see Fig. 1) and 14

(see Fig. 2) are shown. See Suplementary Figures 1–3 for results

from the rest of the patients. The patient number is indicated at

the upper left portion of the figure. (A) Examples of focal dis-

charges. When two different discharges were present, they

were independently illustrated. The left column corresponds to

the left discharge, and the right column corresponds to the right

discharge. (B) Examples of the generalized discharges. (C) Dis-

charges submitted to source analyses with sLORETA in a differ-

ent time window. (D) sLORETA solutions map showing the

current densities according to their Brodmann’s areas. (E)

sLORETA solutions in a anatomical template (superior panel)

and in a 3D rendering (middle panel). When there were two

different discharges, the left discharge is depicted in the pink

scale, and the right discharge is depicted in the jet or cold scale

in (D) and (E). The inferior panel shows the VBM analysis

results. The VBM results (colored areas) are superimposed on

an anatomical template. The colors represent the number of

standard deviations compared with the controls, as indicated by

the scale in the right inferior portion of the figure. Green scale

was used for abnormalities in the left (L) hemisphere and hot

scale for abnormalities in the right (R) hemisphere. Patient 4

(see Fig. 1) had predominantly areas of atrophy and patient 14

(see Fig. 2) had areas of increased gray matter volumes. Statisti-

cal analysis was performed with a corrected P < 0.05 (false dis-

covery rate). Figures are depicted in the neurological convention

(right on right).

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No previous studies have combined quantitative techni-ques, such as sLORETA and VBM analysis, to evaluateIGE patients. The concordance between these methodsdemonstrates that focal cortical abnormalities are presentin some IGE patients. The pathophysiology of absence andmyoclonic seizures are mainly related to the thalamocorti-cal circuitry, and the findings described here reflect theinvolvement of the cortex in the mechanism behind theseseizures in this group of patients. According to experimen-tal studies, myoclonic and especially absence seizures mayhave a focal onset in the cortex [Avanzini et al., 2000; Mee-ren et al., 2002]. The discharge rapidly spreads to involvethe thalamus. The interaction between the thalamus andcortex may be responsible for the maintenance of the sei-zure [Meeren et al., 2002].

Using quantitative EEG source analysis, a previousstudy investigated 25 absence seizures in five subjects[Holmes et al., 2004]. The authors observed that absenceseizures likely involved selective cortical networks, includ-ing the orbital frontal and mesial frontal regions, which

supported earlier findings [Meeren et al., 2002]. Our EEGquantitative evaluation was performed in interictal dis-charges but is inline with this investigation, showing clearfocal discharges localized mainly in the frontal lobes (11 of17 patients, 65%). This predominance may be responsiblefor the frontal lobe abnormalities observed in IGE patientsin previous neuroimaging investigations of IGE [Bettinget al., 2006a; Woermann et al., 1999].

Visual inspection of MRI scans showed that there weremild abnormalities in IGE patients [Betting et al., 2006b].Using a standardized protocol for investigating epilepsy,we studied the MRI scans of 134 IGE patients. Of thesepatients, 33 (24%) showed abnormalities, most of whichwere nonspecific, including arachnoid cysts, cortical atro-phy and signal alterations [Betting et al., 2006b]. In ourcurrent investigation, patients with abnormal MRI scansafter visual analysis were excluded. The subtle and hetero-geneous gray matter abnormalities that we detected inhere may be associated with the minor abnormalitiesdescribed previously.

Figure 2.

Two EEG samples and source analysis and individual VBM com-

parisons of IGE patients with 20 controls searching for gray mat-

ter abnormalities. The data for patients 4 (see Fig. 1) and 14

(see Fig. 2) are shown. See Suplementary Figures 1–3 for results

from the rest of the patients. The patient number is indicated at

the upper left portion of the figure. (A) Examples of focal dis-

charges. When two different discharges were present, they

were independently illustrated. The left column corresponds to

the left discharge, and the right column corresponds to the right

discharge. (B) Examples of the generalized discharges. (C) Dis-

charges submitted to source analyses with sLORETA in a differ-

ent time window. (D) sLORETA solutions map showing the

current densities according to their Brodmann’s areas. (E)

sLORETA solutions in a anatomical template (superior panel)

and in a 3D rendering (middle panel). When there were two dif-

ferent discharges, the left discharge is depicted in the pink scale,

and the right discharge is depicted in the jet or cold scale in (D)

and (E). The inferior panel shows the VBM analysis results. The

VBM results (colored areas) are superimposed on an anatomical

template. The colors represent the number of standard devia-

tions compared with the controls, as indicated by the scale in

the right inferior portion of the figure. Green scale was used for

abnormalities in the left (L) hemisphere and hot scale for abnor-

malities in the right (R) hemisphere. Patient 4 (see Fig. 1) had

predominantly areas of atrophy and patient 14 (see Fig. 2) had

areas of increased gray matter volumes. Statistical analysis was

performed with a corrected P < 0.05 (false discovery rate). Fig-

ures are depicted in the neurological convention (right on right).

r Betting et al. r

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Quantitative MRI investigations in IGE patients haveprovided several results. A study using VBM-individualanalysis showed that 25% (five of 20) of JME patients hadareas of gray matter abnormalities. Three patients showedareas with decreased gray matter volumes, and twopatients had areas with increased gray matter volumes.The group comparison showed increased gray matter vol-umes localized in the frontal lobes [Woermann et al.,1999]. Our previous VBM-group analysis revealedincreased gray matter areas in the frontal basal and mesio-frontal regions [Betting et al., 2006a]. Investigations of thebrain using VBM in IGE patients showed increased graymatter volumes in the bilateral superior mesiofrontalregions, as well as decreased gray matter concentration inthe prefrontal region and in other structures, including thecerebellum, and the frontal, parietal and temporal cortex[Ciumas and Savic, 2006; Kim et al., 2007; Tae et al., 2006].Increased gray matter was also detected in the thalamususing VBM [Betting et al., 2006a]. Decreased thalamic vol-umes were also reported in patients with IGE [Chan et al.,2006; Ciumas and Savic, 2006; Helms et al., 2006]. Tha-lamic atrophy was correlated with the duration of the epi-lepsy in JME patients [Kim et al., 2007]. Additionally,other morphological abnormalities have been reportedsuch as flattening of the brain in the craniocaudal direction

in GTCS patients, reduced cortical thickness at the frontaland temporal lobes which was correlated with disease du-ration in JME patients and smaller subcortical volumeswhich were mainly characterized by putaminal atrophy invarious IGE subsyndromes [Savic et al., 1998; Seeck et al.,2005; Tae et al., 2008]. These apparently conflicting find-ings parallel the heterogeneity observed in IGE patients.Our investigation also demonstrates this variability. Forexample, patients number 4 and 22 have similar ages anddramatically different GSW discharges in their EEGs. VBMshowed diffuse areas of increased gray matter volumes inthe frontal cortex, in the thalamus, caudate nucleus andbasal ganglia. The volumetry profiles also showed diver-gent patterns, with patient 4 having predominant areas ofatrophy and patient 22 showing areas of increased vol-umes in comparison to the controls. Moreover, some ofthe patients we studied did not present abnormalities inVBM analysis, indicating that another major mechanismcaused the disease in these individuals. In addition to thevariations between the EEG results and structural findingsin IGE patients, there is significant variability in the clini-cal observations between patients. The spatial diversity ofthe gray matter abnormalities and the absence of a patternfor each IGE subsyndrome also emphasize the heterogene-ity and may be related with the clinical variability

Figure 3.

ROI volumetry maps of patients 4 and 14. The volumes are

standardized based on the normalized volumes of the control

group (Z-score). Each color corresponds to the number of

standard deviations compared with controls, as indicated by the

colorbar. Hot colors correspond to areas of increased volumes,

and cold colors correspond to decreased volumes compared

with the controls. The superior panel shows an anatomical ren-

dering and the inferior panel shows an inflated rendering. Patient

number is displayed on the left. See the online Supporting Infor-

mation for the volumetry maps from the rest of the patients.

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observed in these patients. Genetic predisposition andenvironmental factors can influence the brain, and thesevariables influence the final phenotype of each IGEpatient. The complex profiles are confirmed by geneticstudies, which showed that most IGE cases have a poly-genic basis. Only a few IGE patients were shown to bedue to a monogenic disorder caused by abnormal neuro-nal GABA receptors or ion channels [Helbig et al., 2008].

The use of individual VBM analysis may have drawbacks.There is an increased rate of false positives results especiallycharacterized by gray matter reduction [Salmond et al.,2002]. Furthermore, previous investigations using VBM forthe evaluation of individual patients with normal MRI wereunable to detect abnormalities [Eriksson et al., 2009; Sal-menpera et al., 2007]. The authors concluded that this meth-odology may not be suitable to detect occult abnormalitiespossibly associated with seizure onset zone in individualpatients with unremarkable standard structural MRI. How-ever, in some cases quantitative MRI may be useful [Sal-menpera et al., 2007]. This problem may be secondary tomethodological issues, specially normalization and statisti-cal significance thresholding [Eriksson et al., 2009]. Individ-ual VBM analysis is less sensitive to areas of greatvariability like the mesial temporal lobes and areas ofdecreased gray matter. Despite of these limitations, studiesusing individual VBM analysis in patients with focal corti-

cal dysplasia showed that this method is able to detect graymatter abnormalities in a majority of patients and theabnormal areas extend beyond the visible lesion [Bonilhaet al., 2006; Colliot et al., 2006]. The patients investigated inhere had predominantly neocortical and areas of increasedgray matter volumes. The use of VBM comparisons in thecontrol group, the use of two distinct methods for sourceanalysis and ROI volumetry also strengthened the findingsof our study. The pattern of structural abnormalityobserved here is in accordance with previous findings andto the hypothesized pathophysiology of IGE.

The abnormal cortical areas detected by morphometricanalyses may correspond to structural defects, such as dys-plasia, which would be in agreement with previous descrip-tions of histopathological abnormalities in patients with JME[Meencke and Janz, 1984]. Additionally, this hypothesis isalso reinforced by the predominance of cases with increasedgray matter in our investigation (88% of the patients withabnormalities, as detected by VBM) and in previous studiesusing VBM-group analysis [Betting et al., 2006; Kim et al.,2007; Woermann et al., 1999]. Another feasible explanationfor the abnormalities detected by VBM analysis is that theseareas may represent changes in neuronal connectivity[Woermann et al., 1999]. Neuronal connectivity changes canbe closely related to neuronal function and have been meas-ured with MRS via reduced concentrations of N-acetyl-aspartate [Savic et al., 2000; Simister et al., 2003].

Focal epilepsies occasionally show generalized dis-charges in EEG recordings as a result of a secondarybisynchrony [Tukel and Jasper, 1952]. Furthermore, aurasare commonly reported in patients with generalized epi-lepsies [Boylan et al., 2006]. These peculiarities can lead toan erroneous epilepsy syndrome diagnosis. In these situa-tions, the exact classification of patients may be difficult,and it is important to be mindful that the EEG and MRIresults may reveal intermediary clinical situations. Basi-cally, classifications try to group common diseases in orderto facilitate the clinical practice, teaching and research. Itmust be remembered that, in some cases as presentedhere, the whole clinical, EEG and MRI picture may revealintermediary situations.

ACKNOWLEDGMENTS

The authors and their immediate family members haveno competing financial interests related to this article.

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